---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '22999'
abstract:
- lang: eng
text: The safety of Reinforcement Learning (RL)-based controllers has become a prominent
research area in recent years, with various approaches being proposed to address
this critical issue. Runtime Safety Assurance (RSA) methods for RL, such as Shielded
RL, provide formal safety guarantees by preventing agents from taking unsafe actions
and suggesting safe alternatives when necessary. However, previous surveys and
reviews on RSA for RL have not thoroughly analysed the challenges and applications
within the industrial sector. This study builds on existing state-of-the-art research
on Shielded RL methods, emphasising its contributions to industrial applications
and offering a domain-specific categorisation. This categorisation highlights
the primary industrial domains utilising Shielded RL, detailing the optimised
functions achieved by RL and the safety functions ensured by the shield. Additionally,
the study presents a categorisation based on environmental features, enabling
readers to assess the complexity of the problems addressed by the techniques studied.
The shield’s attributes are analysed for each work, identifying key trends in
their application, including their adaptability to new scenarios. Finally, a basic
categorisation model for Shielded RL approaches, grounded in industrial safety
standards, is introduced. This model serves as a baseline for future studies aiming
to evaluate the maturity level of the works reviewed.
acknowledgement: "This work was supported in part by the SAFEXPLAIN project under
Grant 101069595.\r\nThis work was supported in part by the ROBOCONS project under
Grant 101235566, from the European Union‘s HORIZON-CL5-2024-D4-02 research and innovation
programme.\r\nThis work was supported in part by the Austrian Science Fund (FWF)
- Reference 10.55776/COE12.\r\nThis work was supported in part by the Intelligent
Systems for Industrial Systems research group of Mondragon Unibertsitatea - Reference
IT1870-26. Department of Science, Universities and Innovation of the Basque Government.\r\nThis
work was supported in part by the European Research Council under Grant No.: ERC-2020-AdG
101020093. This work was also supported by the ISTA Responsible AI Program, made
possible through the support of Garrett Camp and the Camp Foundation ."
article_number: '200729'
article_processing_charge: Yes
article_type: review
author:
- first_name: Haritz
full_name: Odriozola-Olalde, Haritz
last_name: Odriozola-Olalde
- first_name: Filip
full_name: Cano Cordoba, Filip
id: 708cad98-e86a-11ef-8098-bdae2d7c6af1
last_name: Cano Cordoba
orcid: 0000-0002-0783-904X
- first_name: Bettina
full_name: Könighofer, Bettina
last_name: Könighofer
- first_name: Nestor
full_name: Arana-Arexolaleiba, Nestor
last_name: Arana-Arexolaleiba
- first_name: Maider
full_name: Zamalloa, Maider
last_name: Zamalloa
- first_name: Jon
full_name: Perez-Cerrolaza, Jon
last_name: Perez-Cerrolaza
citation:
ama: 'Odriozola-Olalde H, Cano Cordoba F, Könighofer B, Arana-Arexolaleiba N, Zamalloa
M, Perez-Cerrolaza J. Shielded reinforcement learning for industrial applications:
A systematic literature survey. Intelligent Systems with Applications.
2026;32. doi:10.1016/j.iswa.2026.200729'
apa: 'Odriozola-Olalde, H., Cano Cordoba, F., Könighofer, B., Arana-Arexolaleiba,
N., Zamalloa, M., & Perez-Cerrolaza, J. (2026). Shielded reinforcement learning
for industrial applications: A systematic literature survey. Intelligent Systems
with Applications. Elsevier. https://doi.org/10.1016/j.iswa.2026.200729'
chicago: 'Odriozola-Olalde, Haritz, Filip Cano Cordoba, Bettina Könighofer, Nestor
Arana-Arexolaleiba, Maider Zamalloa, and Jon Perez-Cerrolaza. “Shielded Reinforcement
Learning for Industrial Applications: A Systematic Literature Survey.” Intelligent
Systems with Applications. Elsevier, 2026. https://doi.org/10.1016/j.iswa.2026.200729.'
ieee: 'H. Odriozola-Olalde, F. Cano Cordoba, B. Könighofer, N. Arana-Arexolaleiba,
M. Zamalloa, and J. Perez-Cerrolaza, “Shielded reinforcement learning for industrial
applications: A systematic literature survey,” Intelligent Systems with Applications,
vol. 32. Elsevier, 2026.'
ista: 'Odriozola-Olalde H, Cano Cordoba F, Könighofer B, Arana-Arexolaleiba N, Zamalloa
M, Perez-Cerrolaza J. 2026. Shielded reinforcement learning for industrial applications:
A systematic literature survey. Intelligent Systems with Applications. 32, 200729.'
mla: 'Odriozola-Olalde, Haritz, et al. “Shielded Reinforcement Learning for Industrial
Applications: A Systematic Literature Survey.” Intelligent Systems with Applications,
vol. 32, 200729, Elsevier, 2026, doi:10.1016/j.iswa.2026.200729.'
short: H. Odriozola-Olalde, F. Cano Cordoba, B. Könighofer, N. Arana-Arexolaleiba,
M. Zamalloa, J. Perez-Cerrolaza, Intelligent Systems with Applications 32 (2026).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: No data was used for the research described in the article.
date_created: 2026-09-27T22:01:51Z
date_published: 2026-09-18T00:00:00Z
date_updated: 2026-10-07T06:25:37Z
day: '18'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1016/j.iswa.2026.200729
ec_funded: 1
fulldoi: https://doi.org/10.1016/j.iswa.2026.200729
has_accepted_license: '1'
intvolume: ' 32'
keyword:
- Reinforcement learning
- Runtime safety assurance
- Shield
- Safety
- Industrial application
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.iswa.2026.200729
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Intelligent Systems with Applications
publication_identifier:
issn:
- 2667-3053
publication_status: epub_ahead
publisher: Elsevier
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: 'Shielded reinforcement learning for industrial applications: A systematic
literature survey'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2026'
...
---
_id: '12976'
abstract:
- lang: eng
text: "3D printing based on continuous deposition of materials, such as filament-based
3D printing, has seen widespread adoption thanks to its versatility in working
with a wide range of materials. An important shortcoming of this type of technology
is its limited multi-material capabilities. While there are simple hardware designs
that enable multi-material printing in principle, the required software is heavily
underdeveloped. A typical hardware design fuses together individual materials
fed into a single chamber from multiple inlets before they are deposited. This
design, however, introduces a time delay between the intended material mixture
and its actual deposition. In this work, inspired by diverse path planning research
in robotics, we show that this mechanical challenge can be addressed via improved
printer control. We propose to formulate the search for optimal multi-material
printing policies in a reinforcement\r\nlearning setup. We put forward a simple
numerical deposition model that takes into account the non-linear material mixing
and delayed material deposition. To validate our system we focus on color fabrication,
a problem known for its strict requirements for varying material mixtures at a
high spatial frequency. We demonstrate that our learned control policy outperforms
state-of-the-art hand-crafted algorithms."
acknowledgement: This work is graciously supported by FWF Lise Meitner (Grant M 3319).
Kang Liao sincerely thank Emiliano Luci, Chunyu Lin, and Yao Zhao for their huge
support.
article_processing_charge: No
author:
- first_name: Kang
full_name: Liao, Kang
last_name: Liao
- first_name: Thibault
full_name: Tricard, Thibault
last_name: Tricard
- first_name: Michael
full_name: Piovarci, Michael
id: 62E473F4-5C99-11EA-A40E-AF823DDC885E
last_name: Piovarci
orcid: 0000-0002-5062-4474
- first_name: Hans-Peter
full_name: Seidel, Hans-Peter
last_name: Seidel
- first_name: Vahid
full_name: Babaei, Vahid
last_name: Babaei
citation:
ama: 'Liao K, Tricard T, Piovarci M, Seidel H-P, Babaei V. Learning deposition policies
for fused multi-material 3D printing. In: 2023 IEEE International Conference
on Robotics and Automation. Vol 2023. IEEE; 2023:12345-12352. doi:10.1109/ICRA48891.2023.10160465'
apa: 'Liao, K., Tricard, T., Piovarci, M., Seidel, H.-P., & Babaei, V. (2023).
Learning deposition policies for fused multi-material 3D printing. In 2023
IEEE International Conference on Robotics and Automation (Vol. 2023, pp. 12345–12352).
London, United Kingdom: IEEE. https://doi.org/10.1109/ICRA48891.2023.10160465'
chicago: Liao, Kang, Thibault Tricard, Michael Piovarci, Hans-Peter Seidel, and
Vahid Babaei. “Learning Deposition Policies for Fused Multi-Material 3D Printing.”
In 2023 IEEE International Conference on Robotics and Automation, 2023:12345–52.
IEEE, 2023. https://doi.org/10.1109/ICRA48891.2023.10160465.
ieee: K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, and V. Babaei, “Learning deposition
policies for fused multi-material 3D printing,” in 2023 IEEE International
Conference on Robotics and Automation, London, United Kingdom, 2023, vol.
2023, pp. 12345–12352.
ista: 'Liao K, Tricard T, Piovarci M, Seidel H-P, Babaei V. 2023. Learning deposition
policies for fused multi-material 3D printing. 2023 IEEE International Conference
on Robotics and Automation. ICRA: International Conference on Robotics and Automation
vol. 2023, 12345–12352.'
mla: Liao, Kang, et al. “Learning Deposition Policies for Fused Multi-Material 3D
Printing.” 2023 IEEE International Conference on Robotics and Automation,
vol. 2023, IEEE, 2023, pp. 12345–52, doi:10.1109/ICRA48891.2023.10160465.
short: K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, V. Babaei, in:, 2023 IEEE
International Conference on Robotics and Automation, IEEE, 2023, pp. 12345–12352.
conference:
end_date: 2023-06-02
location: London, United Kingdom
name: 'ICRA: International Conference on Robotics and Automation'
start_date: 2023-05-29
date_created: 2023-05-16T09:14:09Z
date_published: 2023-07-04T00:00:00Z
date_updated: 2025-04-15T07:43:52Z
day: '04'
ddc:
- '004'
department:
- _id: BeBi
doi: 10.1109/ICRA48891.2023.10160465
external_id:
isi:
- '001048371104068'
file:
- access_level: open_access
checksum: daeaa67124777d88487f933ea3f77164
content_type: application/pdf
creator: mpiovarc
date_created: 2023-05-16T09:12:05Z
date_updated: 2023-05-16T09:12:05Z
file_id: '12977'
file_name: Liao2023.pdf
file_size: 5367986
relation: main_file
success: 1
file_date_updated: 2023-05-16T09:12:05Z
fulldoi: https://doi.org/10.1109/ICRA48891.2023.10160465
has_accepted_license: '1'
intvolume: ' 2023'
isi: 1
keyword:
- reinforcement learning
- deposition
- control
- color
- multi-filament
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
page: 12345-12352
project:
- _id: eb901961-77a9-11ec-83b8-f5c883a62027
grant_number: M03319
name: Perception-Aware Appearance Fabrication
publication: 2023 IEEE International Conference on Robotics and Automation
publication_identifier:
eisbn:
- '9798350323658'
issn:
- 1050-4729
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning deposition policies for fused multi-material 3D printing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2023
year: '2023'
...